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<@ULVA73B9P> What is the proper way to call a secr...
# ask-marvin
c
@Marvin What is the proper way to call a secret block in prefect 3, i am getting errors with the syntax provided in the UI?
m
In Prefect 3.x, you can utilize secret values within Blocks by using the 
SecretStr
field type provided by Pydantic. This ensures that sensitive information such as API keys or passwords are obfuscated in the UI and logs. Here's a basic example of how to implement this using the Prefect documentation: 1. Define your block with fields that need to be secret: - Use 
SecretStr
for fields where you want to keep the values hidden. 2. Register the block with a schema if needed, enabling you to create block documents. To give you a sample, imagine you have an AWS credentials block that uses 
SecretStr
for the secret key:
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python
from pydantic import BaseModel, SecretStr

class AwsCredentials(BaseModel):
    aws_access_key_id: str
    aws_secret_access_key: SecretStr

aws_credentials = AwsCredentials(
    aws_access_key_id="your_access_key_id",
    aws_secret_access_key=SecretStr("your_secret_access_key")
)
In this example, when logged, the value of 
aws_secret_access_key
will be obfuscated. Also, the Prefect docs for blocks can be quite helpful: Prefect 3 Blocks Documentation. If you're facing syntax errors, make sure that your implementation matches the latest documentation specifications. Sometimes, UI instructions might lag a bit behind the official documentation updates. Ah, technology, always having a little game of catch-up with itself! 😊
c
@Marvin Prefect UI provides this:
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from prefect.blocks.system import Secret

Secret(value="sk-1234567890").save("BLOCK_NAME", overwrite=True)

secret_block = Secret.load("BLOCK_NAME")

# Access the stored secret
secret_block.get()
But i get an error with .get(): TypeError: cannot unpack non-iterable coroutine object
m
It seems you're encountering a 
TypeError
because you're trying to directly call 
.get()
on what is likely a coroutine in Prefect 3.x. In async Python, coroutines need to be awaited. This could be why you're running into a "cannot unpack non-iterable coroutine object" error. Here’s how you might properly handle it: 1. Use 
await
when working with async functions or coroutines. This means you need to make sure your surrounding code is running within an async function. Here's a modified example that should help:
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python
import asyncio
from prefect.blocks.system import Secret

async def access_secret_block():
    # Load the secret block
    secret_block = await Secret.load("BLOCK_NAME")
    
    # Access the stored secret
    secret_value = await secret_block.get()
    print(secret_value)

# Run the async function
asyncio.run(access_secret_block())
By wrapping your code inside an async function and using 
await
, you permit the coroutine to be executed properly. It's a little like preparing tea: you must wait for the kettle to boil before pouring. 😉 For more details, you can check Prefect's documentation on blocks: Prefect 3 Blocks Documentation.
c
@Marvin RuntimeError: asyncio.run() cannot be called from a running event loop
m
Ah, the classic asyncio conundrum. This error occurs because 
asyncio.run()
is designed to be used when you're not already in an event loop—such as when running code directly in a notebook or interactively. Here's how you can handle it within an existing event loop, typically encountered in Jupyter notebooks: Use 
nest_asyncio
to allow nested use of 
asyncio.run()
or use 
await
directly if inside an async function. 1. Using `nest_asyncio`: You can install 
nest_asyncio
to help with nested async event loops.
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bash
   pip install nest-asyncio
Then, modify your code as follows:
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python
   import nest_asyncio
   nest_asyncio.apply()

   import asyncio
   from prefect.blocks.system import Secret

   async def access_secret_block():
       # Load the secret block
       secret_block = await Secret.load("BLOCK_NAME")
       
       # Access the stored secret
       secret_value = await secret_block.get()
       print(secret_value)

   # Run the async function
   await access_secret_block()
2. Directly Using 
await
inside Jupyter Notebooks: If you're working in an async context, like an async function within Jupyter, just ensure your context is async:
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python
   import asyncio
   from prefect.blocks.system import Secret

   # Ensure this cell or context is already running an event loop
   async def my_main():
       secret_block = await Secret.load("BLOCK_NAME")
       secret_value = await secret_block.get()
       print(secret_value)

   await my_main()  # Directly use 'await' here
Pick your preferred approach based on your environment, and hopefully, the secret will be revealed without hiccups this time. Remember, APIs and secrets often play a bit hard to get! 😊
c
@Marvin Is there a github link to an example code snippet of someone using Secret.load in functioning code?
m
It seems like I'm having trouble accessing GitHub to find a specific example for you. However, you can manually search for examples by visiting the GitHub repository for Prefect and using the search bar there with queries like "Secret.load" or "secret block example". If you copy that query and adjust it in GitHub's search, you might find user implementations or examples in issues and pull requests that demonstrate how to properly use 
Secret.load
in functioning Prefect code. Just remember, though—API keys and secrets, no matter how small, deserve the utmost care. 😊
c
@Marvin When use "prefect deploy" and go through the CLI, the CLI gets stuck on: "? Would you like to add another schedule? [y/n] (n): n" When i enter n, the CLI gets hung up and does nothing. What could be causing this?
m
The issue you're experiencing with the Prefect CLI hanging when asked about adding another schedule might be related to how the CLI processes input or how the local environment is configured for handling CLI interactive prompts. Unfortunately, I couldn't fetch specific GitHub issues that discuss this directly due to access restrictions, but here are a few things you might try based on common CLI troubleshooting steps: 1. Ensure CLI Compatibility: - Confirm you're using a compatible version of Prefect for your environment. Sometimes, incompatibilities can cause unexpected issues. 2. Update Prefect: - Ensure that you have the latest version of Prefect by running:
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pip install --upgrade prefect
3. Check Prefect Logs: - Look for any logs that might indicate what's going wrong during the execution. They might give hints if it's a configuration or environment issue. 4. Use Non-Interactive Mode: - If interactive mode consistently hangs, see if you can use command-line arguments or a YAML configuration to set the schedule programmatically, thus bypassing the interactive prompts. 5. Running Prefect Commands in a Clean Shell: - Sometimes existing processes or environment variables might interfere with Prefect. It's worth trying the commands in a fresh terminal session. 6. Community and Support: - If the problem persists, consider reaching out to the Prefect Community for support. They might have encountered and solved similar issues. For more details on deploying with Prefect, you can check out their documentation. Remember, like all smart but stubborn systems, sometimes a gentle nudge (restart or update) can do wonders! 😊
c
@Marvin how do i Check Prefect Logs:
m
To check Prefect logs, you'll generally want to look at both local logs generated during your flow runs as well as logs available through the Prefect UI if you're utilizing Prefect Cloud or a local Prefect server. Here's how you can access them: 1. Local Logs: - When you run a flow locally, Prefect usually logs information to your console output. Ensure your logging level is set to capture enough detail. You can set the logging level by configuring the logging module in your script:
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python
     import prefect
     from prefect import task, Flow

     logger = prefect.context.get("logger")
     logger.setLevel("DEBUG")

     @task
     def my_task():
         <http://logger.info|logger.info>("This is an info message")
         logger.debug("This is a debug message")

     with Flow("my-flow") as flow:
         my_task()

     flow.run()
- This will direct flow-related logs to your console, so running your script with an appropriate logging level can help capture more detailed logs. 2. Prefect UI (Cloud or Local Server): - If your flows are running in Prefect Cloud or a local Prefect server, you can access logs via the UI. - Navigate to the Prefect UI dashboard and select the flow or flow run you are interested in. Each flow run should have log details accessible from its run details page. - Look for options like "Logs" or "Flow Run Logs" to access detailed logging information. 3. Configuring Additional Logging: - If needed, you can also configure Prefect to write log files by changing its configuration. This might involve editing 
~/.prefect/config.toml
or setting environment variables to redirect log outputs to more permanent storage. Checking logs enables you to debug issues, understand flow execution states, and verify task outcomes. Always remember the wisdom of logs: what happens in Prefect, stays on the log... unless you fix it. 😉
c
@Marvin using prefect 3 how do i build a deployment from a yaml file
m
In Prefect 3.x, you can build a deployment from a YAML file with ease, thanks to the flexibility Prefect offers. Here's how you can do it: 1. Prepare Your YAML File: - Ensure your YAML file (commonly named 
prefect.yaml
) is structured correctly. In Prefect 3.x, it can be located anywhere in your directory and can have any name, as long as it ends with 
.yaml
. 2. Using the CLI to Deploy: - If your 
prefect.yaml
file is located in your project’s root directory, you can use the 
prefect deploy
command directly:
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bash
     prefect deploy
- If your YAML file is not in the root directory or has a different name, use the 
--prefect-file
argument to specify the path to your YAML file:
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bash
     prefect deploy --prefect-file path/to/my_file.yaml
3. Version Control and Managing Deployments: - The 
prefect.yaml
file not only facilitates deployment creation but also helps manage multiple deployments and allows you to version-control your deployment configurations. 4. Additional Options: - The CLI offers interactive prompts to help tailor the deployment process, or you can directly define actions like 
build
in your YAML file for more controlled operations. For further details, you can refer to the Prefect 3.x documentation on YAML deployments: Prefect YAML Deployment Documentation. Think of YAML and Prefect as your trustworthy sidekicks on the data-flow automation journey; what they lack in charisma, they make up in reliability! 😊
c
@Marvin I use this command but the CLI still prompts me with the interactive mode: prefect deploy --prefect-file load_netsuite-deployment.yaml
m
It looks like you're trying to execute a deployment in Prefect using a YAML file and wish to avoid interactive prompts, but still encountering them. Here are some strategies that could help streamline this process to minimize or bypass interactive prompts: 1. YAML Completeness: Ensure your 
load_netsuite-deployment.yaml
is fully specified with all necessary details like schedules, parameters, and infrastructure details. Prefect prompts are often triggered if some required information is not completely defined in the YAML file. 2. Default Values in YAML: Make sure that all sections of the YAML file provide clear defaults to prevent any ambiguity that could prompt the CLI interaction. This includes: - Ensuring all variables have values. - Specifying any blocks or infrastructure needed. - Detailing any schedules if applicable. 3. Use Command-Line Flags: If applicable, use CLI flags to specify information typically requested in interactive prompts. However, this depends on the specific setup and whether Prefect's CLI supports it for all fields. 4. Check for Known Issues: Verify against any existing issues/notes in the Prefect docs specific to version compatibility, as CLI behavior can vary with updates or patches. For a detailed understanding of the deployment process and YAML configuration, you can refer to Prefect's guide: Prefect YAML Deployment Documentation. If the issue persists, it might be worth engaging with Prefect's community forums or reviewing any recent changelogs for CLI tools to understand if there's a known issue or patch concerning your problem. تعامل مع الطوارئ بشكل هادئ وسريع أيضًا!
c
@Marvin What is missing from the following deployment.yaml for prefect 3. Or can you provide me an example of a functiong deplyoment.yaml from github?
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name: netsuite-load
entrypoint: load_netsuite.py:main
work_pool:
  name: default-agent-pool
  work_queue_name: default
schedule:
  cron: "0 0 * * *"
tags:
  - prod
  - netsuite
description: "test desc"
m
I ran into a bit of an obstacle trying to fetch specific examples from GitHub directly. However, I can guide you based on typical configurations and common pitfalls in Prefect 3.x 
deployment.yaml
files. Here's a more complete example based on your provided YAML and what might be required or beneficial to include:
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yaml
name: netsuite-load
entrypoint: load_netsuite.py:main   # Ensure this points to the correct Python file and function
work_pool:
  name: default-agent-pool          # Verify that this work pool is set up in your Prefect environment
  work_queue_name: default          # Ensure this queue exists in Prefect as well
schedule:
  cron: "0 0 * * *"                 # Confirm that the schedule format is recognized by Prefect
tags:
  - prod
  - netsuite
description: "test desc"

# Optional additions
parameters:                         # If your flow requires parameters, define them here
  example_param: some_value

infra_overrides:                    # This section can override default infrastructure settings
  cpu: "500m"
  memory: "1Gi"

storage:                            # Define a storage block if needed, like S3 or Docker
  block: s3/storage-block-name
Things to consider: - Entry Point: Ensure that 
load_netsuite.py
and 
main
are the correct path and function names. - Work Pool and Queue: Double-check that the pool and queue names correspond to the correct resources in your Prefect setup. - Parameters and Overrides: Include any necessary parameters and infrastructure overrides to ensure your flow runs with the desired configuration. - Storage: If your flows rely on external storage or need specific storage configuration, make sure to define it. If you're still running into issues, reviewing the Prefect documentation can also be helpful: Prefect YAML Deployment Docs. Sometimes, reading YAML is like deciphering a secret code; if all else fails, reaching out to the Prefect community can reveal hidden insights. 😊
c
@Marvin interactive CLI gets hung up on deployment build?
m
When your CLI gets stuck on the deployment build step in Prefect 3.x, it could be related to several common issues. Here are some steps and checks you can perform to troubleshoot the problem: 1. Check YAML Configuration: - Ensure the 
prefect.yaml
file is correctly configured and contains all necessary fields. Missing or incorrect values can cause processes to hang. 2. CLI Environment: - Run your commands in a clean terminal session. It’s possible that lingering background processes or environment variables can interfere with the CLI operations. 3. Network Issues: - If you're deploying to a cloud environment, ensure your network settings are correct and that your system can reach necessary Prefect Cloud or API endpoints. 4. Inspect Prefect Logs: - Examine any logs or console outputs for error messages that can provide more insights. Sometimes, the logs will report specific issues hindering the deployment process. 5. Interrupt Safely and Retry: - If it gets stuck, try to safely interrupt the process (usually Ctrl+C) and then attempt the deployment again. 6. Check for Known Bugs: - Ensure your Prefect version is up to date, as bugs are frequently patched. Check Prefect's release notes for any known issues or necessary updates. 7. Manual Checks: - Verify the existence of referenced resources like work pools and queues in the Prefect UI. Misconfigured or missing resources will cause hang-ups. For detailed guidance, you can refer to the Prefect 3.x documentation on deployment: Prefect Deployment Documentation. Navigating the quirks of a CLI and YAML can be a bit like deciphering alien hieroglyphs, but the right nudge (and a correctly formatted YAML) can work wonders! 😊
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